Please use this identifier to cite or link to this item:
https://ah.lib.nccu.edu.tw/handle/140.119/23009
DC Field | Value | Language |
---|---|---|
dc.creator | 陳樹衡;T.Yu;T.-W. Kuo | zh_TW |
dc.date | 2004-07 | en_US |
dc.date.accessioned | 2009-01-09T03:21:15Z | - |
dc.date.available | 2009-01-09T03:21:15Z | - |
dc.date.issued | 2009-01-09T03:21:15Z | - |
dc.identifier.uri | https://nccur.lib.nccu.edu.tw/handle/140.119/23009 | - |
dc.description.abstract | Using GP with lambda abstraction module mechanism to generate technical trading rules based on S&P 500 index, we find strong evidence of excess returns over buy-and-hold after transaction cost on the testing period from 1989 to 2002. The rules can be interpreted easily; each uses a combination of one to four widely used technical indicators to make trading decisions. The consensus among GP rules is high, with most of the time 80% of the evolved rules give the same decision. The GP rules give high transaction frequency. Regardless of market climate, they are able to identify opportunities to make profitable trades and out-perform buy-and-hold | - |
dc.format | application/ | en_US |
dc.language | en | en_US |
dc.language | en-US | en_US |
dc.language.iso | en_US | - |
dc.relation | No 200, Computing in Economics and Finance 2004 from Society for Computational Economics | en_US |
dc.title | Using Genetic Programming with Lambda Abstraction to Find Technical Trading Rule | en_US |
dc.type | conference | en |
item.fulltext | With Fulltext | - |
item.languageiso639-1 | en_US | - |
item.openairecristype | http://purl.org/coar/resource_type/c_18cf | - |
item.cerifentitytype | Publications | - |
item.grantfulltext | open | - |
item.openairetype | conference | - |
Appears in Collections: | 會議論文 |
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index.html | 115 B | HTML2 | View/Open |
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